Amazon Technologies, Inc.

Ali Caner Turkmen

Ashwin Swaminathan

Raghavan Manmatha

Jill Blue Lin

Mohammed Talal Yassar Azam

Ketan Vijayvargiya

Hao Yang

Lenon Alexander Minorics

David Kernert

Siqi Deng

Patrick Bloebaum

Mohammed Saad Ather

Ankur Mehrotra

Sadanand Murthy Sachidananda

Laurent Callot

Peter Goetz

Syed Ahsan Ishtiaque

Kailash Budhathoki

Varad Gunjal

Christopher Michael Russell

Matthaeus Kleindessner

Shashank Srivastava

Dominik Janzing

The present disclosure generally relates to systems and methods for searching media content. In some implementation examples, a search system receives an input query, generates a query embedding of the input query, and generates a bias mitigation transformation associated with a sensitive attribute. Based on the query embedding and the bias mitigation transformation, the search system generates a transformed query embedding that suppresses at least a portion of the query embedding related to the sensitive attribute. Using the transformed query embedding, the search system executes a similarity search in a media embedding model to identify one or more media embeddings that are similar to the transformed query embedding and transmits the one or more media embeddings.

Mitigating bias in multimodal models via query transformation

Techniques for anomaly and causality detection are described. An example includes receiving time series data; performing anomaly detection on the received time series data to detect at least one anomaly using an anomaly detection model; detecting a causal relationship between measures, wherein a set of measures are related when a first of the set of measures has a causal influence on a second of the set of measures, wherein a single time series is a metric and a measure is a numerical or categorical quantity a metric describes; and outputting a result of the anomaly and causality relationship detections.

Anomaly and causality detection

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Berlin, Germany

Ali Caner Turkmen